Multiple Music Sentiment Classification Model Based on Convolutional Neural Network

Yang, Jing; Zeng, Fanfu; Wang, Yong; Yu, Huan; Zhang, Le

doi:10.1007/978-981-15-0118-0_48

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…A research hotspot is using emotion classification technology to solve practical problems in social hot topics of professional migrant workers. The method of statistical emotion words is used in literature [ 10 ] to calculate emotions under fixed topics, which can be applied to trend prediction and yields good results. Hong et al [ 11 ] calculate the emotional time series using the ratio of Weibo number to positive and negative emotional words obtained from Twitter data every day, and then use a self-organizing fuzzy neural network to predict the Dow Jones Industrial Average.…”

Section: Related Workmentioning

confidence: 99%

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Pang

Bao

2022

Computational Intelligence and Neuroscience

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Text makes up a large portion of network data because it is the vehicle for people’s direct expression of emotions and opinions. How to analyze and mine these emotional text data has become a hot topic of concern in academia and industry in recent years. The online LDA (Latent Dirichlet Allocation) model is used in this paper to train the social hot topic data of professional migrant workers on the same time slice, and the subtopic evolution and intensity are obtained. The topic development is divided into four categories, and the classification model is created using SVM (Support Vector Machine). Instead of decision makers, a virtual human with sensibility and rationality is built using a hierarchical emotional cognitive model to solve multiobjective optimization problems interactively. It analyzes human body structure and emotional signals, and then combines them with visual and physiological signals to create multimodal emotional data. An example is used to demonstrate the effectiveness of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Pang

Bao

2022

Computational Intelligence and Neuroscience

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“…Literature [2] proposes a dual-channel attention network Chinese text sentiment analysis model based on mixed word embedding, which combines coarse data reasoning and semantic association rules to compensate for the loss of emotion in feature extraction of short texts and solve the problem of Chinese word polysemy. Literature [3] proposes a dual-input channel gated convolutional neural network model combined with emotional dictionary, which combines emotional dictionary and gated convolutional neural network to solve the problem that noise is easy to generate when Chinese data discrimination. However, due to the great differences between different languages, the same model may have different results in different languages, so there will be similar problems when dealing with English.…”

Section: Introductionmentioning

confidence: 99%

Bilingual multi-feature sentiment analysis based on relationship degree

Ding

Zhang

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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Aiming at the problem that the monolingual corpus (Chinese or English) is affected by the sentence structure in the sentiment analysis task, resulting in a large deviation of the extracted context information, and the traditional model does not consider the dependence between aspect words and context information words, which makes the sentiment classification inaccurate, a bilingual multi-feature sentiment analysis model with the degree of fusion relationship is proposed. Firstly, the attention mechanism is used to calculate the attention relationship degree of the aspect word in the sentence, and the Hadama product operation is performed with the context information to obtain the dependent information of the aspect word. Then, the Bidirectional Gated Recurrent Unit (BiGRU) is used to learn the global serialization information of the sentence to obtain the overall information characteristics of the sentence. Next, through the attention mechanism, the overall information characteristics and the aspect word-dependent features are integrated to obtain single-language information. Finally, through the attention mechanism, the overall information characteristics and the aspect word-dependent features are integrated to obtain single-language information. Experimental results on the public datasets Sem Eval2014 Task4 and AI Challenger 2018 show that the model can obtain sentiment information based on aspect words more comprehensively, and has higher accuracy of text sentiment polarity classification than the comparison model.

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Multiple Music Sentiment Classification Model Based on Convolutional Neural Network

Cited by 2 publications

References 7 publications

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Bilingual multi-feature sentiment analysis based on relationship degree

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